This is exciting!
So in this article I'll introduce you to a new model for envisioning Big* Data and provide a structured explanation of why it's so valuable to humans.
Data Volume and Span, a model for data
Data Volume comes primarily from market share (having lots of customers) and by product use (customers doing lots of stuff). Volume is just about how much data is being produced, and the rate at which it is produced. Sufficient data volume is needed to gain a truly representative understanding of the subject being examined.
Data Span, on the other hand, comes by recording different types of data; how many different types of data there are going into the data warehouse. Sufficient data span is needed to enable a variety of data correlations from which valuable insights can be derived.
Of course, data volume and span both hinge on having a product, product component, sales process, or other system that actually records data. If you sell tacos from a truck you might find it trickier to record customer data than Facebook does, since your core product isn't connected directly to the internet generating data every time somebody likes it.
Now that we know the two fundamental ways in which data can be considered "Big", next we'll take a look at how all that data that gets recorded in database tables becomes transformed into knowledge within a living human brain.
There's too much data just to read it top to bottom. Moreover, there's next to no value in looking at an event. You'd be able to deduce that: a person, paid for tickets, for a show, from a vendor, at a time, for a price. That's meaningless. More valuable to know how many people bought tickets for that show, what the price changes over time were, how well the show sold compared to other shows, whether the time that the show played was advantageous to selling more expensive tickets, and other information that can help your business make more money for less effort. Such information is called "insights".
But to attain knowledge isn't as simple as looking at the screen. I need to fully understand what I'm looking at. While that depends on the "human-readability" of the information presented, it also depends on my competence, as a human, in interpreting what I see in objective ways.
The result is not just big — it's huge.
* Big, small, or medium-sized data doesn't really matter in terms of how it works. Changing the data volume or span won't deviate or alter the process described above. A huge part of that, as you can see, is in the effectiveness of the data handling system in its ability to record data accurately and display it meaningfully to a human operator. We'll talk about Business Intelligence solutions in another article.
Open Data Institute (2015) Open data means business: UK innovation across sectors and regions. London, UK. Available at http://theodi.org/open-data-means-business
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